system

Abstract

The system according to this embodiment aims to visualize changes in a child's emotions and behavior and notify the guardian. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, and a notification unit. The collection unit collects conversation and behavior history data, health data, and digital content usage data. The analysis unit comprehensively analyzes the data collected by the collection unit. The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. The notification unit notifies the guardian based on the data visualized by the visualization unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to detect changes in children's emotions and behaviors at an early stage and take appropriate measures.

[0005] The system according to the embodiment aims to visualize changes in children's emotions and behaviors and notify guardians.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a visualization unit, and a notification unit. The collection unit collects conversation and behavioral history data, health data, and digital content usage data. The analysis unit comprehensively analyzes the data collected by the collection unit. The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. The notification unit notifies parents based on the data visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can visualize changes in a child's emotions and behavior and notify the guardian. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The growth support system according to an embodiment of the present invention is a system that utilizes an AI agent to comprehensively monitor and support the growth of children by integrating and analyzing conversation and behavioral history data, health data (physical and emotional), and digital content usage data obtained in various environments of children, such as home, school, cram school, and club activities. The growth support system uses AI to analyze children's emotions in real time and detect signs of stress and worries early. The AI ​​engages in natural conversations with children as needed, providing emotional support while being attentive to the child. For example, when a child talks about what happened at school, the AI ​​analyzes the changes in their emotions and engages in appropriate conversation. Next, the growth support system periodically generates a growth report that visualizes changes in the child's emotions and behavior. This report includes topics and suggestions to deepen conversations between parents and children, supporting dialogue tailored to the child's interests and challenges. For example, if a child starts a new hobby, the growth support system reflects its progress and emotional changes in the report. Furthermore, if a sudden change in emotions or behavior (e.g., a sudden increase in stress, lethargy) is observed, the growth support system will issue an alert notification to the parent according to its importance. The system detects signs of bullying, school refusal, and depression without overlooking them and responds quickly. For example, if a child is unwilling to go to school, the growth support system analyzes the cause and notifies the parents. Through this mechanism, the growth support system aims to deepen parent-child relationships and trust in educational settings by creating an environment where children can grow with peace of mind, and to resolve problems such as bullying and school refusal at an early stage. In this way, the growth support system can monitor and support a child's growth from multiple perspectives.

[0029] The growth support system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, and a notification unit. The collection unit collects conversation and behavioral history data, health data, and digital content usage data. The collection unit can collect conversation and behavioral history data such as voice data, text data, and behavioral logs. The collection unit can also collect health data such as heart rate, body temperature, and exercise level. Furthermore, the collection unit can collect digital content usage data such as app usage history and website browsing history. For example, the collection unit can collect conversational data using speech recognition technology and behavioral history data using behavioral sensors. The collection unit can use wearable devices to collect health data. The collection unit can analyze application usage history to collect digital content usage data. The analysis unit comprehensively analyzes the data collected by the collection unit. The analysis unit can comprehensively analyze conversation and behavioral history data, health data, and digital content usage data using, for example, data integration methods and analysis algorithms. For example, the analysis unit can use machine learning algorithms to detect changes in a child's emotions and behavior from the collected data. The analysis unit can centrally manage and analyze data from different data sources during data integration. The analysis unit can preprocess data, remove noise, and improve data quality. The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. The visualization unit can visualize changes in emotions and behavior using methods such as graphs and heatmaps. For example, the visualization unit can display changes in emotions in a time-series graph and changes in behavior in a heatmap. The visualization unit can devise visual representations of data to visualize changes in emotions and behavior in an easy-to-understand manner. The visualization unit can provide interactive dashboards to visualize changes in emotions and behavior. The notification unit notifies parents based on the data visualized by the visualization unit. The notification unit can notify parents using methods such as email notifications and app notifications.For example, the notification unit can alert parents when it detects a sudden change in emotions or behavior. The notification unit can customize the content of notifications, prioritizing information that is important to parents. The notification unit can also adjust the timing of notifications, ensuring that parents are notified at the appropriate time. As a result, the growth support system according to this embodiment can monitor and support a child's growth from multiple perspectives.

[0030] The data collection unit collects conversation and behavioral history data, health data, and digital content usage data. Specifically, it can collect conversation and behavioral history data such as voice data, text data, and behavioral logs. For example, it can collect conversational data using speech recognition technology and behavioral history data using behavioral sensors. Speech recognition technology is used to convert the child's speech into text data and analyze the content of the conversation and changes in emotions. Behavioral sensors record the child's movements and activity patterns, allowing for a detailed understanding of their daily behavioral history. The data collection unit can use wearable devices to collect health data such as heart rate, body temperature, and exercise level. Wearable devices are used to monitor the child's physical condition in real time and detect changes in their health. For example, they can collect important health data such as fluctuations in heart rate and increases in body temperature. Furthermore, the data collection unit can also collect digital content usage data such as app usage history and website browsing history. By analyzing application usage history, it is possible to understand what kind of digital content the child is interested in and analyze trends in their digital behavior. In this way, the data collection unit can comprehensively collect diverse data on the child and provide data that forms the basis of a growth support system. The collected data is stored in a central database, making it accessible to the analysis and visualization departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department comprehensively analyzes the data collected by the collection department. Specifically, it can comprehensively analyze conversation and behavioral history data, health data, and digital content usage data using data integration methods and analysis algorithms. For example, it can use machine learning algorithms to detect changes in children's emotions and behavior from the collected data. Machine learning algorithms can analyze large amounts of data and find patterns and trends. For example, it can detect changes in emotions from voice data and identify changes in activity patterns from behavioral logs. In data integration, the analysis department can centrally manage and analyze data from different data sources. It can preprocess data to remove noise and improve data quality. For example, it can remove background noise from collected voice data and correct typographical errors in text data. This allows the analysis department to perform analyses based on accurate and reliable data. Furthermore, the analysis department can utilize historical data and statistical information to evaluate long-term trends and risks. For example, it can predict what impact specific behavioral patterns will have in the future based on past behavioral history data. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and prediction, improving the reliability and security of the entire system.

[0032] The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. Specifically, it can visualize changes in emotions and behavior using methods such as graph displays and heatmap displays. For example, changes in emotions can be displayed in a time-series graph, and changes in behavior can be displayed in a heatmap. The time-series graph visually shows changes in emotions over time, helping parents understand trends in their children's emotions. The heatmap visually shows the places and times when specific behaviors occur frequently, helping to analyze behavioral patterns. The visualization unit can devise visual representations of data to visualize changes in emotions and behavior in an easy-to-understand manner. For example, it can use colors and shapes to indicate the intensity and type of emotions, making them intuitively understandable. By providing an interactive dashboard, parents can freely manipulate data of interest and view detailed information. For example, they can filter data to focus on specific periods or events and display detailed analysis results. In this way, the visualization unit can provide parents with information to intuitively understand their children's growth and changes and take appropriate action.

[0033] The notification unit notifies parents based on data visualized by the visualization unit. Specifically, it can notify parents using methods such as email notifications and app notifications. For example, it can send an alert notification to parents if a sudden change in emotions or behavior is detected. The notification unit can customize the content of notifications and prioritize information that is important to parents. For example, it can notify parents about important changes in their child's health or changes in specific behavioral patterns. It can adjust the timing of notifications to notify parents at the appropriate time. For example, it can refrain from sending notifications during times when parents cannot respond immediately, such as at night or while at work, and send notifications at the appropriate time. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. As a result, the notification unit can provide parents with quick and reliable instructions, and monitor and support the child's growth from multiple perspectives. By receiving notifications, parents can understand their child's situation in real time and take appropriate action. As a result, the growth support system according to this embodiment can monitor and support the child's growth from multiple perspectives.

[0034] The analysis unit can analyze changes in emotions and behavior in real time. For example, the analysis unit can increase the data update frequency and use real-time processing algorithms to instantly grasp changes in emotions and behavior. For example, the analysis unit can set the data update frequency to every second, collect and analyze data in real time. The analysis unit can instantly detect changes in data using real-time processing algorithms and grasp changes in emotions and behavior. Based on the data analyzed in real time, the analysis unit can instantly grasp the child's condition. This allows for instant understanding of the child's condition by analyzing changes in emotions and behavior in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collected in real time into a generating AI and have the generating AI perform the analysis of changes in emotions and behavior.

[0035] The visualization unit can generate growth reports periodically. For example, the visualization unit can set the report generation frequency to weekly or monthly and generate growth reports regularly. For example, the visualization unit can generate a growth report every Monday and provide it to parents. The visualization unit can reflect changes in the child's emotions and behavior in the growth report and include topics and suggestions to deepen conversations between parents and children. For example, if a child starts a new hobby, the visualization unit can reflect its progress and changes in emotions in the report. The visualization unit can provide information in the growth report to support conversations tailored to the child's interests and challenges. For example, the visualization unit can include information about topics the child is interested in in the report to deepen conversations between parents and children. In this way, by generating growth reports periodically, the child's growth can be visualized and conversations between parents and children can be deepened. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input the collected data into a generating AI and have the generating AI perform the generation of growth reports.

[0036] The notification unit can send alert notifications to parents when there are sudden changes in emotions or behavior. For example, the notification unit can set thresholds for changes in emotions or behavior and send an alert notification when those thresholds are exceeded. For example, the notification unit can send an alert notification to parents when stress levels rise sharply. The notification unit can customize the content of alert notifications and prioritize information that is important to parents. For example, the notification unit can send alert notifications that include detailed information about changes in the child's emotions or behavior. The notification unit can adjust the timing of alert notifications and notify parents at the appropriate time. For example, the notification unit can send alert notifications while the child is at school. This allows for early detection and response to problems by notifying parents when there are sudden changes in emotions or behavior. Some or all of the above processes in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input data on changes in emotions and behavior into a generating AI and have the generating AI generate alert notifications.

[0037] The data collection unit can collect data obtained from multiple environments of children, such as home, school, cram school, and club activities. For example, the data collection unit can collect conversation and behavioral history data at home, health data at school, and digital content usage data at cram school. For example, the data collection unit can collect conversations at home using speech recognition technology and health data at school using wearable devices. The data collection unit can collect digital content usage data at cram school by analyzing application usage history. The data collection unit can also collect behavioral history data from club activities using behavioral sensors. In this way, by collecting data obtained from various environments of children, such as home, school, cram school, and club activities, it is possible to obtain multifaceted data on children. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from home, school, cram school, and club activities into a generating AI and have the generating AI perform the data collection.

[0038] The analysis unit can detect signs of bullying, truancy, and depression. For example, the analysis unit can analyze changes in behavioral patterns and emotional changes to detect signs of bullying, truancy, and depression. For example, the analysis unit can analyze a child's behavioral history data to detect a tendency to be unwilling to go to school. The analysis unit can analyze emotional data to detect a sudden increase in stress levels. The analysis unit can also analyze digital content usage data to detect signs that a child is excessively dependent on certain content. This allows for the early detection and intervention of a child's problems by detecting signs of bullying, truancy, and depression. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of bullying, truancy, and depression.

[0039] The data collection unit can analyze a child's past behavioral history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data related to behaviors that a child has frequently performed in the past. The data collection unit can predict from a child's past behavioral history that a child's behavior will be more active during specific time periods and collect data during those times. The data collection unit can also analyze a child's past behavioral patterns and adjust the data collection method if a change in behavior is observed. This allows the optimal data collection method to be selected by analyzing a child's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's past behavioral history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the child's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas the child is currently interested in. The data collection unit can adjust the type and amount of data collected according to the child's living situation (e.g., during school test periods). If the child's areas of interest change, the data collection unit can also filter the data collected accordingly. This allows for the collection of more relevant data by filtering data based on the child's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data related to their activities and conversations at school. If the child is at home, the data collection unit can prioritize the collection of data related to their activities at home. If the child is participating in club activities, the data collection unit can also prioritize the collection of data related to those activities. By considering the child's geographical location during data collection, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, if a child is very active on social media, the data collection unit will collect data related to that activity. If a child is inactive on social media, the data collection unit can reduce the frequency of collecting data related to that activity. The data collection unit can also analyze the content of a child's social media activity and collect data related to specific topics. This allows for the collection of relevant data by analyzing a child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data, and a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. For behavioral data, it can apply a behavioral analysis algorithm. For health data, it can also apply a health analysis algorithm. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to previously collected data. The analysis unit can also adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The visualization unit can predict current data by referring to past data during visualization. For example, the visualization unit can predict changes in current emotions based on past emotional data. The visualization unit can predict changes in current behavior based on past behavioral data. The visualization unit can also predict changes in current health status based on past health data. In this way, future changes can be foreseen by predicting current data by referring to past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input past data into a generating AI and have the generating AI perform predictions of current data.

[0048] The visualization unit can apply different visualization methods to each data category during visualization. For example, for emotion data, the visualization unit can display changes in emotion as a graph. For behavior data, the visualization unit can display changes in behavior as a chart. For health data, the visualization unit can display changes in health status as a heatmap. By applying different visualization methods to each data category, a more easily understandable visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data categories into a generating AI and have the generating AI execute the application of an appropriate visualization method.

[0049] The visualization unit can analyze changes in visualization based on the data collection period during visualization. For example, the visualization unit can analyze changes in visualization based on recently collected data. The visualization unit can visualize current data while referring to previously collected data. The visualization unit can also analyze changes in visualization in stages according to the data collection period. This allows for understanding temporal changes by analyzing changes in visualization based on the data collection period. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data collection period into a generating AI and have the generating AI perform the analysis of changes in visualization.

[0050] The visualization unit can analyze the visualization by referring to relevant market data during visualization. For example, the visualization unit can analyze changes in the visualization based on relevant market data. The visualization unit can visualize the current data while referring to relevant market data. The visualization unit can also analyze changes in the visualization in stages according to the relevant market data. This allows for analysis from a broader perspective by analyzing the visualization by referring to relevant market data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization analysis.

[0051] The notification unit can adjust the level of detail of notifications based on the importance of the data when sending notifications. For example, the notification unit can provide detailed notifications for highly important data, and simplified notifications for less important data. The notification unit can also adjust the level of detail of notifications in stages according to the importance of the data. This allows for the appropriate notification of important information by adjusting the level of detail of notifications based on the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0052] The notification unit can apply different notification algorithms depending on the data category when sending notifications. For example, for emotion data, the notification unit can send notifications that emphasize changes in emotion. For behavior data, the notification unit can send notifications that emphasize changes in behavior. For health data, the notification unit can also send notifications that emphasize changes in health status. By applying different notification algorithms depending on the data category, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate notification algorithm.

[0053] The notification unit can determine the priority of notifications based on the data collection timing at the time of notification. For example, the notification unit can determine the priority of notifications based on recently collected data. The notification unit can also determine the priority of notifications based on current data while referring to previously collected data. The notification unit can also adjust the priority of notifications in stages according to the data collection timing. This allows for priority notification of the latest information by determining the priority of notifications based on the data collection timing. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the notification priority.

[0054] The notification unit can adjust the order of notifications based on the relevance of the data when sending notifications. For example, the notification unit can prioritize notifications based on highly relevant data. The notification unit can postpone notifications based on less relevant data. The notification unit can also adjust the order of notifications in stages according to the relevance of the data. This allows important information to be notified preferentially by adjusting the order of notifications based on the relevance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The growth support system can further track a child's learning progress and provide feedback to maximize learning effectiveness. For example, the data collection unit collects the child's learning data, and the analysis unit analyzes that data to evaluate learning progress. The visualization unit displays learning progress in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers with appropriate feedback according to learning progress. In this way, the growth support system can effectively support a child's learning and maximize learning outcomes.

[0057] The growth support system can further assist in the development of children's social skills. For example, the data collection unit collects data on children's friendships and group activities, and the analysis unit analyzes this data to evaluate the development of their social skills. The visualization unit displays the development of social skills in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the development of their social skills. In this way, the growth support system can effectively support the development of children's social skills.

[0058] The growth support system can further support the development of children's creativity. For example, the data collection unit collects data on children's creative activities and hobbies, and the analysis unit analyzes this data to evaluate the development of their creativity. The visualization unit displays the development of creativity in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the development of creativity. In this way, the growth support system can effectively nurture children's creativity.

[0059] The growth support system can further assist in the development of children's motor skills. For example, the data collection unit collects children's motor data, and the analysis unit analyzes that data to evaluate the progress of their motor skills development. The visualization unit displays the progress of motor skills development in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the progress of their motor skills. In this way, the growth support system can effectively support the development of children's motor skills.

[0060] The growth support system can further monitor a child's sleep patterns and support healthy lifestyle habits. For example, the data collection unit collects the child's sleep data, and the analysis unit analyzes that data to evaluate sleep patterns. The visualization unit displays the sleep patterns in graphs and charts and provides them to the parents. The notification unit can notify parents with appropriate feedback based on the sleep patterns. In this way, the growth support system can effectively support healthy lifestyle habits in children.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects conversation and behavioral history data, health data, and digital content usage data. For example, it collects conversation and behavioral history data such as voice data, text data, and behavioral logs; health data such as heart rate, body temperature, and exercise level; and digital content usage data such as app usage history and website browsing history. The collection unit collects conversational data using speech recognition technology and behavioral history data using behavioral sensors. Wearable devices can be used to collect health data, and application usage history can be analyzed to collect digital content usage data. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it comprehensively analyzes conversation and behavioral history data, health data, and digital content usage data using data integration methods and analysis algorithms. It uses machine learning algorithms to detect changes in children's emotions and behavior from the collected data. In data integration, it centrally manages and analyzes data from different data sources. It preprocesses the data to remove noise and improve data quality. Step 3: The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. For example, it visualizes changes in emotions and behavior using methods such as graphs and heatmaps. Changes in emotions are displayed in a time-series graph, and changes in behavior are displayed in a heatmap. The visual representation of the data is devised to be easy to understand. An interactive dashboard can be provided. Step 4: The notification unit notifies parents based on the data visualized by the visualization unit. For example, it notifies parents using methods such as email notifications or app notifications. It alerts parents when a sudden change in emotion or behavior is detected. It customizes the content of notifications to prioritize information that is important to parents. It adjusts the timing of notifications to notify parents at the appropriate time.

[0063] (Example of form 2) The growth support system according to an embodiment of the present invention is a system that utilizes an AI agent to comprehensively monitor and support the growth of children by integrating and analyzing conversation and behavioral history data, health data (physical and emotional), and digital content usage data obtained in various environments of children, such as home, school, cram school, and club activities. The growth support system uses AI to analyze children's emotions in real time and detect signs of stress and worries early. The AI ​​engages in natural conversations with children as needed, providing emotional support while being attentive to the child. For example, when a child talks about what happened at school, the AI ​​analyzes the changes in their emotions and engages in appropriate conversation. Next, the growth support system periodically generates a growth report that visualizes changes in the child's emotions and behavior. This report includes topics and suggestions to deepen conversations between parents and children, supporting dialogue tailored to the child's interests and challenges. For example, if a child starts a new hobby, the growth support system reflects its progress and emotional changes in the report. Furthermore, if a sudden change in emotions or behavior (e.g., a sudden increase in stress, lethargy) is observed, the growth support system will issue an alert notification to the parent according to its importance. The system detects signs of bullying, school refusal, and depression without overlooking them and responds quickly. For example, if a child is unwilling to go to school, the growth support system analyzes the cause and notifies the parents. Through this mechanism, the growth support system aims to deepen parent-child relationships and trust in educational settings by creating an environment where children can grow with peace of mind, and to resolve problems such as bullying and school refusal at an early stage. In this way, the growth support system can monitor and support a child's growth from multiple perspectives.

[0064] The growth support system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, and a notification unit. The collection unit collects conversation and behavioral history data, health data, and digital content usage data. The collection unit can collect conversation and behavioral history data such as voice data, text data, and behavioral logs. The collection unit can also collect health data such as heart rate, body temperature, and exercise level. Furthermore, the collection unit can collect digital content usage data such as app usage history and website browsing history. For example, the collection unit can collect conversational data using speech recognition technology and behavioral history data using behavioral sensors. The collection unit can use wearable devices to collect health data. The collection unit can analyze application usage history to collect digital content usage data. The analysis unit comprehensively analyzes the data collected by the collection unit. The analysis unit can comprehensively analyze conversation and behavioral history data, health data, and digital content usage data using, for example, data integration methods and analysis algorithms. For example, the analysis unit can use machine learning algorithms to detect changes in a child's emotions and behavior from the collected data. The analysis unit can centrally manage and analyze data from different data sources during data integration. The analysis unit can preprocess data, remove noise, and improve data quality. The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. The visualization unit can visualize changes in emotions and behavior using methods such as graphs and heatmaps. For example, the visualization unit can display changes in emotions in a time-series graph and changes in behavior in a heatmap. The visualization unit can devise visual representations of data to visualize changes in emotions and behavior in an easy-to-understand manner. The visualization unit can provide interactive dashboards to visualize changes in emotions and behavior. The notification unit notifies parents based on the data visualized by the visualization unit. The notification unit can notify parents using methods such as email notifications and app notifications.For example, the notification unit can alert parents when it detects a sudden change in emotions or behavior. The notification unit can customize the content of notifications, prioritizing information that is important to parents. The notification unit can also adjust the timing of notifications, ensuring that parents are notified at the appropriate time. As a result, the growth support system according to this embodiment can monitor and support a child's growth from multiple perspectives.

[0065] The data collection unit collects conversation and behavioral history data, health data, and digital content usage data. Specifically, it can collect conversation and behavioral history data such as voice data, text data, and behavioral logs. For example, it can collect conversational data using speech recognition technology and behavioral history data using behavioral sensors. Speech recognition technology is used to convert the child's speech into text data and analyze the content of the conversation and changes in emotions. Behavioral sensors record the child's movements and activity patterns, allowing for a detailed understanding of their daily behavioral history. The data collection unit can use wearable devices to collect health data such as heart rate, body temperature, and exercise level. Wearable devices are used to monitor the child's physical condition in real time and detect changes in their health. For example, they can collect important health data such as fluctuations in heart rate and increases in body temperature. Furthermore, the data collection unit can also collect digital content usage data such as app usage history and website browsing history. By analyzing application usage history, it is possible to understand what kind of digital content the child is interested in and analyze trends in their digital behavior. In this way, the data collection unit can comprehensively collect diverse data on the child and provide data that forms the basis of a growth support system. The collected data is stored in a central database, making it accessible to the analysis and visualization departments. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis department comprehensively analyzes the data collected by the collection department. Specifically, it can comprehensively analyze conversation and behavioral history data, health data, and digital content usage data using data integration methods and analysis algorithms. For example, it can use machine learning algorithms to detect changes in children's emotions and behavior from the collected data. Machine learning algorithms can analyze large amounts of data and find patterns and trends. For example, it can detect changes in emotions from voice data and identify changes in activity patterns from behavioral logs. In data integration, the analysis department can centrally manage and analyze data from different data sources. It can preprocess data to remove noise and improve data quality. For example, it can remove background noise from collected voice data and correct typographical errors in text data. This allows the analysis department to perform analyses based on accurate and reliable data. Furthermore, the analysis department can utilize historical data and statistical information to evaluate long-term trends and risks. For example, it can predict what impact specific behavioral patterns will have in the future based on past behavioral history data. This allows the analysis department to not only grasp the situation in real time but also to handle long-term risk management and prediction, improving the reliability and security of the entire system.

[0067] The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. Specifically, it can visualize changes in emotions and behavior using methods such as graph displays and heatmap displays. For example, changes in emotions can be displayed in a time-series graph, and changes in behavior can be displayed in a heatmap. The time-series graph visually shows changes in emotions over time, helping parents understand trends in their children's emotions. The heatmap visually shows the places and times when specific behaviors occur frequently, helping to analyze behavioral patterns. The visualization unit can devise visual representations of data to visualize changes in emotions and behavior in an easy-to-understand manner. For example, it can use colors and shapes to indicate the intensity and type of emotions, making them intuitively understandable. By providing an interactive dashboard, parents can freely manipulate data of interest and view detailed information. For example, they can filter data to focus on specific periods or events and display detailed analysis results. In this way, the visualization unit can provide parents with information to intuitively understand their children's growth and changes and take appropriate action.

[0068] The notification unit notifies parents based on data visualized by the visualization unit. Specifically, it can notify parents using methods such as email notifications and app notifications. For example, it can send an alert notification to parents if a sudden change in emotions or behavior is detected. The notification unit can customize the content of notifications and prioritize information that is important to parents. For example, it can notify parents about important changes in their child's health or changes in specific behavioral patterns. It can adjust the timing of notifications to notify parents at the appropriate time. For example, it can refrain from sending notifications during times when parents cannot respond immediately, such as at night or while at work, and send notifications at the appropriate time. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. As a result, the notification unit can provide parents with quick and reliable instructions, and monitor and support the child's growth from multiple perspectives. By receiving notifications, parents can understand their child's situation in real time and take appropriate action. As a result, the growth support system according to this embodiment can monitor and support the child's growth from multiple perspectives.

[0069] The analysis unit can analyze changes in emotions and behavior in real time. For example, the analysis unit can increase the data update frequency and use real-time processing algorithms to instantly grasp changes in emotions and behavior. For example, the analysis unit can set the data update frequency to every second, collect and analyze data in real time. The analysis unit can instantly detect changes in data using real-time processing algorithms and grasp changes in emotions and behavior. Based on the data analyzed in real time, the analysis unit can instantly grasp the child's condition. This allows for instant understanding of the child's condition by analyzing changes in emotions and behavior in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collected in real time into a generating AI and have the generating AI perform the analysis of changes in emotions and behavior.

[0070] The visualization unit can generate growth reports periodically. For example, the visualization unit can set the report generation frequency to weekly or monthly and generate growth reports regularly. For example, the visualization unit can generate a growth report every Monday and provide it to parents. The visualization unit can reflect changes in the child's emotions and behavior in the growth report and include topics and suggestions to deepen conversations between parents and children. For example, if a child starts a new hobby, the visualization unit can reflect its progress and changes in emotions in the report. The visualization unit can provide information in the growth report to support conversations tailored to the child's interests and challenges. For example, the visualization unit can include information about topics the child is interested in in the report to deepen conversations between parents and children. In this way, by generating growth reports periodically, the child's growth can be visualized and conversations between parents and children can be deepened. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input the collected data into a generating AI and have the generating AI perform the generation of growth reports.

[0071] The notification unit can send alert notifications to parents when there are sudden changes in emotions or behavior. For example, the notification unit can set thresholds for changes in emotions or behavior and send an alert notification when those thresholds are exceeded. For example, the notification unit can send an alert notification to parents when stress levels rise sharply. The notification unit can customize the content of alert notifications and prioritize information that is important to parents. For example, the notification unit can send alert notifications that include detailed information about changes in the child's emotions or behavior. The notification unit can adjust the timing of alert notifications and notify parents at the appropriate time. For example, the notification unit can send alert notifications while the child is at school. This allows for early detection and response to problems by notifying parents when there are sudden changes in emotions or behavior. Some or all of the above processes in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input data on changes in emotions and behavior into a generating AI and have the generating AI generate alert notifications.

[0072] The data collection unit can collect data obtained from multiple environments of children, such as home, school, cram school, and club activities. For example, the data collection unit can collect conversation and behavioral history data at home, health data at school, and digital content usage data at cram school. For example, the data collection unit can collect conversations at home using speech recognition technology and health data at school using wearable devices. The data collection unit can collect digital content usage data at cram school by analyzing application usage history. The data collection unit can also collect behavioral history data from club activities using behavioral sensors. In this way, by collecting data obtained from various environments of children, such as home, school, cram school, and club activities, it is possible to obtain multifaceted data on children. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from home, school, cram school, and club activities into a generating AI and have the generating AI perform the data collection.

[0073] The analysis unit can detect signs of bullying, truancy, and depression. For example, the analysis unit can analyze changes in behavioral patterns and emotional changes to detect signs of bullying, truancy, and depression. For example, the analysis unit can analyze a child's behavioral history data to detect a tendency to be unwilling to go to school. The analysis unit can analyze emotional data to detect a sudden increase in stress levels. The analysis unit can also analyze digital content usage data to detect signs that a child is excessively dependent on certain content. This allows for the early detection and intervention of a child's problems by detecting signs of bullying, truancy, and depression. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the detection of signs of bullying, truancy, and depression.

[0074] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, if a child is stressed, the data collection unit can increase the frequency of data collection and collect more detailed data. If a child is relaxed, the data collection unit can decrease the frequency of data collection and collect only the minimum necessary data. If a child is excited, the data collection unit can also adjust the timing of data collection and record emotional changes in detail. This allows for the collection of more appropriate data by adjusting the timing of data collection based on the child's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's emotional data into a generating AI and have the generating AI adjust the timing of data collection.

[0075] The data collection unit can analyze a child's past behavioral history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data related to behaviors that a child has frequently performed in the past. The data collection unit can predict from a child's past behavioral history that a child's behavior will be more active during specific time periods and collect data during those times. The data collection unit can also analyze a child's past behavioral patterns and adjust the data collection method if a change in behavior is observed. This allows the optimal data collection method to be selected by analyzing a child's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's past behavioral history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter data based on the child's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas the child is currently interested in. The data collection unit can adjust the type and amount of data collected according to the child's living situation (e.g., during school test periods). If the child's areas of interest change, the data collection unit can also filter the data collected accordingly. This allows for the collection of more relevant data by filtering data based on the child's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate a child's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a child is stressed, the data collection unit will prioritize collecting data related to stress. If a child is relaxed, the data collection unit can prioritize collecting data related to relaxation. If a child is excited, the data collection unit can also prioritize collecting data related to excitement. In this way, important data can be collected preferentially by determining the priority of data to collect based on the child's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's emotional data into a generating AI and have the generating AI determine the priority of data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data related to their activities and conversations at school. If the child is at home, the data collection unit can prioritize the collection of data related to their activities at home. If the child is participating in club activities, the data collection unit can also prioritize the collection of data related to those activities. By considering the child's geographical location during data collection, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, if a child is very active on social media, the data collection unit will collect data related to that activity. If a child is inactive on social media, the data collection unit can reduce the frequency of collecting data related to that activity. The data collection unit can also analyze the content of a child's social media activity and collect data related to specific topics. This allows for the collection of relevant data by analyzing a child's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0080] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a child is stressed, the analysis unit can highlight data related to stress. If a child is relaxed, the analysis unit can highlight data related to relaxation. If a child is excited, the analysis unit can also highlight data related to excitement. By adjusting the presentation of the analysis based on the child's emotions, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotional data into a generating AI and have the generating AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data, and a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to emotion data. For behavioral data, it can apply a behavioral analysis algorithm. For health data, it can also apply a health analysis algorithm. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0083] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is stressed, the analysis unit can perform a short, concise analysis. If the child is relaxed, the analysis unit can perform a detailed analysis. If the child is excited, the analysis unit can also perform an analysis with visually stimulating effects. By adjusting the length of the analysis based on the child's emotions, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the child's emotional data into a generating AI and have the generating AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can also analyze current data while referring to previously collected data. The analysis unit can also adjust the priority of analysis in stages according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis step by step according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The visualization unit can estimate a child's emotions and adjust the display method of the visualization based on the estimated emotions. For example, if a child is stressed, the visualization unit can provide a simple and highly visible display method. If a child is relaxed, the visualization unit can provide a display method that includes detailed information. If a child is excited, the visualization unit can also provide a display method that adds visually stimulating effects. This allows for a more appropriate display by adjusting the display method of the visualization based on the child's emotions. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input child emotion data into a generating AI and have the generating AI adjust the display method of the visualization.

[0087] The visualization unit can predict current data by referring to past data during visualization. For example, the visualization unit can predict changes in current emotions based on past emotional data. The visualization unit can predict changes in current behavior based on past behavioral data. The visualization unit can also predict changes in current health status based on past health data. In this way, future changes can be foreseen by predicting current data by referring to past data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input past data into a generating AI and have the generating AI perform predictions of current data.

[0088] The visualization unit can apply different visualization methods to each data category during visualization. For example, for emotion data, the visualization unit can display changes in emotion as a graph. For behavior data, the visualization unit can display changes in behavior as a chart. For health data, the visualization unit can display changes in health status as a heatmap. By applying different visualization methods to each data category, a more easily understandable visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data categories into a generating AI and have the generating AI execute the application of an appropriate visualization method.

[0089] The visualization unit can estimate a child's emotions and adjust the importance of the visualization based on the estimated emotions. For example, if a child is stressed, the visualization unit can highlight data related to stress. If a child is relaxed, the visualization unit can highlight data related to relaxation. If a child is excited, the visualization unit can also highlight data related to excitement. In this way, important information can be highlighted by adjusting the importance of the visualization based on the child's emotions. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the child's emotion data into a generating AI and have the generating AI perform the adjustment of the importance of the visualization.

[0090] The visualization unit can analyze changes in visualization based on the data collection period during visualization. For example, the visualization unit can analyze changes in visualization based on recently collected data. The visualization unit can visualize current data while referring to previously collected data. The visualization unit can also analyze changes in visualization in stages according to the data collection period. This allows for understanding temporal changes by analyzing changes in visualization based on the data collection period. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data collection period into a generating AI and have the generating AI perform the analysis of changes in visualization.

[0091] The visualization unit can analyze the visualization by referring to relevant market data during visualization. For example, the visualization unit can analyze changes in the visualization based on relevant market data. The visualization unit can visualize the current data while referring to relevant market data. The visualization unit can also analyze changes in the visualization in stages according to the relevant market data. This allows for analysis from a broader perspective by analyzing the visualization by referring to relevant market data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization analysis.

[0092] The notification unit can estimate the child's emotions and adjust the notification method based on the estimated emotions. For example, if the child is stressed, the notification unit can provide a detailed notification to the parent. If the child is relaxed, the notification unit can provide a simplified notification to the parent. If the child is agitated, the notification unit can also provide a rapid notification to the parent. This allows for more appropriate notifications by adjusting the notification method based on the child's emotions. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the child's emotion data into a generating AI and have the generating AI adjust the notification method.

[0093] The notification unit can adjust the level of detail of notifications based on the importance of the data when sending notifications. For example, the notification unit can provide detailed notifications for highly important data, and simplified notifications for less important data. The notification unit can also adjust the level of detail of notifications in stages according to the importance of the data. This allows for the appropriate notification of important information by adjusting the level of detail of notifications based on the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the notifications.

[0094] The notification unit can apply different notification algorithms depending on the data category when sending notifications. For example, for emotion data, the notification unit can send notifications that emphasize changes in emotion. For behavior data, the notification unit can send notifications that emphasize changes in behavior. For health data, the notification unit can also send notifications that emphasize changes in health status. By applying different notification algorithms depending on the data category, more appropriate notifications become possible. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate notification algorithm.

[0095] The notification unit can estimate the child's emotions and determine the priority of notifications based on the estimated emotions. For example, if the child is stressed, the notification unit will prioritize notifications related to stress. If the child is relaxed, the notification unit will prioritize notifications related to relaxation. If the child is excited, the notification unit may also prioritize notifications related to excitement. In this way, important notifications can be prioritized by determining the priority of notifications based on the child's emotions. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the child's emotion data into a generating AI and have the generating AI perform the determination of notification priorities.

[0096] The notification unit can determine the priority of notifications based on the data collection timing at the time of notification. For example, the notification unit can determine the priority of notifications based on recently collected data. The notification unit can also determine the priority of notifications based on current data while referring to previously collected data. The notification unit can also adjust the priority of notifications in stages according to the data collection timing. This allows for priority notification of the latest information by determining the priority of notifications based on the data collection timing. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the notification priority.

[0097] The notification unit can adjust the order of notifications based on the relevance of the data when sending notifications. For example, the notification unit can prioritize notifications based on highly relevant data. The notification unit can postpone notifications based on less relevant data. The notification unit can also adjust the order of notifications in stages according to the relevance of the data. This allows important information to be notified preferentially by adjusting the order of notifications based on the relevance of the data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The growth support system can further track a child's learning progress and provide feedback to maximize learning effectiveness. For example, the data collection unit collects the child's learning data, and the analysis unit analyzes that data to evaluate learning progress. The visualization unit displays learning progress in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers with appropriate feedback according to learning progress. In this way, the growth support system can effectively support a child's learning and maximize learning outcomes.

[0100] The growth support system can further assist in the development of children's social skills. For example, the data collection unit collects data on children's friendships and group activities, and the analysis unit analyzes this data to evaluate the development of their social skills. The visualization unit displays the development of social skills in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the development of their social skills. In this way, the growth support system can effectively support the development of children's social skills.

[0101] The growth support system can further support the development of children's creativity. For example, the data collection unit collects data on children's creative activities and hobbies, and the analysis unit analyzes this data to evaluate the development of their creativity. The visualization unit displays the development of creativity in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the development of creativity. In this way, the growth support system can effectively nurture children's creativity.

[0102] The growth support system can further assist in the development of children's motor skills. For example, the data collection unit collects children's motor data, and the analysis unit analyzes that data to evaluate the progress of their motor skills development. The visualization unit displays the progress of motor skills development in graphs and charts and provides this information to parents and teachers. The notification unit can notify parents and teachers of appropriate feedback according to the progress of their motor skills. In this way, the growth support system can effectively support the development of children's motor skills.

[0103] The growth support system can further monitor a child's sleep patterns and support healthy lifestyle habits. For example, the data collection unit collects the child's sleep data, and the analysis unit analyzes that data to evaluate sleep patterns. The visualization unit displays the sleep patterns in graphs and charts and provides them to the parents. The notification unit can notify parents with appropriate feedback based on the sleep patterns. In this way, the growth support system can effectively support healthy lifestyle habits in children.

[0104] The growth support system can further estimate a child's emotions and adjust learning progress based on those estimated emotions. For example, the data collection unit collects the child's emotional data, and the analysis unit analyzes that data to evaluate changes in emotions. The visualization unit displays changes in emotions in graphs and charts and provides them to parents and teachers. The notification unit can notify feedback to adjust learning progress in response to changes in emotions. In this way, the growth support system can effectively adjust learning progress based on the child's emotions.

[0105] The growth support system can further estimate a child's emotions and support the development of social skills based on those estimated emotions. For example, the data collection unit collects data on the child's emotions, and the analysis unit analyzes that data to evaluate changes in emotions. The visualization unit displays changes in emotions in graphs and charts and provides them to parents and teachers. The notification unit can notify feedback to support the development of social skills in response to changes in emotions. In this way, the growth support system can effectively support the development of social skills based on the child's emotions.

[0106] The growth support system can further estimate a child's emotions and support the development of their creativity based on those estimated emotions. For example, the data collection unit collects the child's emotional data, and the analysis unit analyzes that data to evaluate changes in emotions. The visualization unit displays changes in emotions in graphs and charts and provides them to parents and teachers. The notification unit can notify children of feedback to support the development of their creativity in response to changes in emotions. In this way, the growth support system can effectively support the development of a child's creativity based on their emotions.

[0107] The growth support system can further estimate a child's emotions and support the development of their motor skills based on those estimated emotions. For example, the data collection unit collects emotional data from the child, and the analysis unit analyzes that data to evaluate changes in emotions. The visualization unit displays the changes in emotions in graphs and charts and provides them to parents and teachers. The notification unit can provide feedback to support the development of motor skills in response to changes in emotions. In this way, the growth support system can effectively support the development of a child's motor skills based on their emotions.

[0108] The growth support system can further estimate a child's emotions and adjust their sleep patterns based on those emotions. For example, the data collection unit collects emotional data from the child, and the analysis unit analyzes that data to evaluate changes in emotions. The visualization unit displays the changes in emotions in graphs and charts and provides them to the parents. The notification unit can provide feedback to adjust sleep patterns in response to changes in emotions. In this way, the growth support system can effectively adjust sleep patterns based on the child's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection unit collects conversation and behavioral history data, health data, and digital content usage data. For example, it collects conversation and behavioral history data such as voice data, text data, and behavioral logs; health data such as heart rate, body temperature, and exercise level; and digital content usage data such as app usage history and website browsing history. The collection unit collects conversational data using speech recognition technology and behavioral history data using behavioral sensors. Wearable devices can be used to collect health data, and application usage history can be analyzed to collect digital content usage data. Step 2: The analysis unit comprehensively analyzes the data collected by the collection unit. For example, it comprehensively analyzes conversation and behavioral history data, health data, and digital content usage data using data integration methods and analysis algorithms. It uses machine learning algorithms to detect changes in children's emotions and behavior from the collected data. In data integration, it centrally manages and analyzes data from different data sources. It preprocesses the data to remove noise and improve data quality. Step 3: The visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the analysis unit. For example, it visualizes changes in emotions and behavior using methods such as graphs and heatmaps. Changes in emotions are displayed in a time-series graph, and changes in behavior are displayed in a heatmap. The visual representation of the data is devised to be easy to understand. An interactive dashboard can be provided. Step 4: The notification unit notifies parents based on the data visualized by the visualization unit. For example, it notifies parents using methods such as email notifications or app notifications. It alerts parents when a sudden change in emotion or behavior is detected. It customizes the content of notifications to prioritize information that is important to parents. It adjusts the timing of notifications to notify parents at the appropriate time.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects conversation and behavioral history data using the camera 42 and microphone 38B of the smart device 14 and works in conjunction with a wearable device to collect health data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The visualization unit visualizes changes in emotions and behavior using the display 40A of the smart device 14. The notification unit is implemented in the control unit 46A of the smart device 14 and notifies the guardian. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects conversation and behavioral history data using the camera 42 and microphone 238 of the smart glasses 214 and works in conjunction with a wearable device to collect health data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The visualization unit visualizes changes in emotions and behavior using the display of the smart glasses 214. The notification unit is implemented in the control unit 46A of the smart glasses 214 and notifies the guardian. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects conversation and behavioral history data using the camera 42 and microphone 238 of the headset terminal 314 and works in conjunction with a wearable device to collect health data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The visualization unit visualizes changes in emotions and behavior using the display 343 of the headset terminal 314. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies the guardian. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, visualization unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects conversation and behavioral history data using the camera 42 and microphone 238 of the robot 414 and works in conjunction with a wearable device to collect health data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and comprehensively analyzes the collected data. The visualization unit visualizes changes in emotions and behavior using the display of the robot 414. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the guardian. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A collection unit that collects conversation and behavioral history data, health data, and digital content usage data, An analysis unit that comprehensively analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the aforementioned analysis unit, The system includes a notification unit that notifies the guardian based on the data visualized by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze changes in emotions and behavior in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned visualization unit, Generate growth reports regularly The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If a sudden change in a child's emotions or behavior is observed, an alert notification will be sent to the parent. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system described in Appendix 1 is characterized in that the collection unit collects data obtained from multiple environments of children, including home, school, cram school, club activities, and other settings. (Note 6) The aforementioned analysis unit is Detecting signs of bullying, school refusal, and depression. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the child's past behavioral history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the child's emotions and adjust the way the analysis is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the child's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, The system estimates the child's emotions and adjusts the visualization display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing data, we refer to past data to predict current data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing data, different visualization methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, The system estimates the child's emotions and adjusts the importance of visualization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, When visualizing data, analyze how the visualization changes based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing the data, we analyze the visualization by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the child's emotions and adjusts the notification method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, The system estimates the child's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending notifications, the system prioritizes notifications based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects conversation and behavioral history data, health data, and digital content usage data, An analysis unit that comprehensively analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes changes in emotions and behavior based on the analysis results obtained by the aforementioned analysis unit, The system includes a notification unit that notifies the guardian based on the data visualized by the visualization unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze changes in emotions and behavior in real time. The system according to feature 1.

3. The aforementioned visualization unit, Generate growth reports regularly The system according to feature 1.

4. The aforementioned notification unit, If a sudden change in a child's emotions or behavior is observed, an alert notification will be sent to the parent. The system according to feature 1.

5. The system according to claim 1, characterized in that the collection unit collects data obtained from multiple environments of children, including home, school, cram school, club activities, and other settings.

6. The aforementioned analysis unit is Detecting signs of bullying, school refusal, and depression. The system according to feature 1.

7. The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the child's past behavioral history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and areas of interest. The system according to feature 1.

Images